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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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import warnings
from typing import Any, Dict, Optional, Tuple, cast

import torch
import torch.nn.functional as F
from torch import nn

from kornia.core.check import KORNIA_CHECK_SHAPE
from kornia.core.utils import dataclass_to_dict, dict_to_dataclass
from kornia.feature.sold2.structures import DetectorCfg, LineMatcherCfg
from kornia.geometry.conversions import normalize_pixel_coordinates

from .backbones import SOLD2Net
from .sold2_detector import LineSegmentDetectionModule, line_map_to_segments, prob_to_junctions

urls: Dict[str, str] = {}
urls["wireframe"] = "http://cmp.felk.cvut.cz/~mishkdmy/models/sold2_wireframe.pth"


class SOLD2(nn.Module):
    r"""nn.Module, which detects and describe line segments in an image.

    This is based on the original code from the paper "SOLD²: Self-supervised
    Occlusion-aware Line Detector and Descriptor". See :cite:`SOLD22021` for more details.

    Args:
        config: Dict specifying parameters. None will load the default parameters,
            which are tuned for images in the range 400~800 px.
        pretrained: If True, download and set pretrained weights to the model.

    Returns:
        The raw junction and line heatmaps, the semi-dense descriptor map,
        as well as the list of detected line segments (ij coordinates convention).

    Example:
        >>> images = torch.rand(2, 1, 64, 64)
        >>> sold2 = SOLD2()
        >>> outputs = sold2(images)
        >>> line_seg1 = outputs["line_segments"][0]
        >>> line_seg2 = outputs["line_segments"][1]
        >>> desc1 = outputs["dense_desc"][0]
        >>> desc2 = outputs["dense_desc"][1]
        >>> matches = sold2.match(line_seg1, line_seg2, desc1[None], desc2[None])

    """

    def __init__(self, pretrained: bool = True, config: Optional[DetectorCfg] = None) -> None:
        if isinstance(config, dict):
            warnings.warn(
                "Usage of config as a plain dictionary is deprecated in favor of"
                " `kornia.features.sold2.structures.DetectorCfg`. The support of plain dictionaries"
                "as config will be removed in kornia v0.8.0 (December 2024).",
                category=DeprecationWarning,
                stacklevel=2,
            )
            config = dict_to_dataclass(cast(Dict[str, Any], config), DetectorCfg)
        super().__init__()
        # Initialize some parameters
        self.config = config if config is not None else DetectorCfg()
        self.config.use_descriptor = True  # Only difference to SOLD2_detector DetectorCfg
        self.grid_size = self.config.grid_size
        self.junc_detect_thresh = self.config.detection_thresh
        self.max_num_junctions = self.config.max_num_junctions

        # Load the pre-trained model
        self.model = SOLD2Net(dataclass_to_dict(self.config))
        if pretrained:
            pretrained_dict = torch.hub.load_state_dict_from_url(urls["wireframe"], map_location=torch.device("cpu"))
            state_dict = self.adapt_state_dict(pretrained_dict["model_state_dict"])
            self.model.load_state_dict(state_dict)
        self.eval()

        # Initialize the line detector
        self.line_detector = LineSegmentDetectionModule(self.config.line_detector_cfg)

        # Initialize the line matcher
        self.line_matcher = WunschLineMatcher(self.config.line_matcher_cfg)

    def forward(self, img: torch.Tensor) -> Dict[str, Any]:
        """Run forward.

        Args:
            img: batched images with shape :math:`(B, 1, H, W)`.

        Returns:
            line_segments: list of N line segments in each of the B images :math:`List[(N, 2, 2)]`.
            junction_heatmap: raw junction heatmap of shape :math:`(B, H, W)`.
            line_heatmap: raw line heatmap of shape :math:`(B, H, W)`.
            dense_desc: the semi-dense descriptor map of shape :math:`(B, 128, H/4, W/4)`.

        """
        KORNIA_CHECK_SHAPE(img, ["B", "1", "H", "W"])
        outputs = {}

        # Forward pass of the CNN backbone
        net_outputs = self.model(img)
        outputs["junction_heatmap"] = net_outputs["junctions"]
        outputs["line_heatmap"] = net_outputs["heatmap"]
        outputs["dense_desc"] = net_outputs["descriptors"]

        # Loop through all images
        lines = []
        for junc_prob, heatmap in zip(net_outputs["junctions"], net_outputs["heatmap"]):
            # Get the junctions
            junctions = prob_to_junctions(junc_prob, self.grid_size, self.junc_detect_thresh, self.max_num_junctions)

            # Run the line detector
            line_map, junctions, _ = self.line_detector.detect(junctions, heatmap)
            lines.append(line_map_to_segments(junctions, line_map))
        outputs["line_segments"] = lines

        return outputs

    def match(
        self, line_seg1: torch.Tensor, line_seg2: torch.Tensor, desc1: torch.Tensor, desc2: torch.Tensor
    ) -> torch.Tensor:
        """Find the best matches between two sets of line segments and their corresponding descriptors.

        Args:
            line_seg1: list of line segments in image 1, with shape [num_lines, 2, 2].
            line_seg2: list of line segments in image 2, with shape [num_lines, 2, 2].
            desc1: semi-dense descriptor map of image 1, with shape [1, 128, H/4, W/4].
            desc2: semi-dense descriptor map of image 2, with shape [1, 128, H/4, W/4].

        Returns:
            A np.array of size [num_lines1] indicating the index in line_seg2 of the matched line,
            for each line in line_seg1. -1 means that the line is not matched.

        """
        return self.line_matcher(line_seg1, line_seg2, desc1, desc2)

    def adapt_state_dict(self, state_dict: Dict[str, Any]) -> Dict[str, Any]:
        del state_dict["w_junc"]
        del state_dict["w_heatmap"]
        del state_dict["w_desc"]
        state_dict["heatmap_decoder.conv_block_lst.2.0.weight"] = state_dict["heatmap_decoder.conv_block_lst.2.weight"]
        state_dict["heatmap_decoder.conv_block_lst.2.0.bias"] = state_dict["heatmap_decoder.conv_block_lst.2.bias"]
        del state_dict["heatmap_decoder.conv_block_lst.2.weight"]
        del state_dict["heatmap_decoder.conv_block_lst.2.bias"]
        return state_dict


class WunschLineMatcher(nn.Module):
    """Class matching two sets of line segments with the Needleman-Wunsch algorithm.

    TODO: move it later in kornia.feature.matching
    """

    def __init__(self, config: Optional[LineMatcherCfg] = None) -> None:
        super().__init__()
        # Initialize the parameters
        if config is None:
            config = LineMatcherCfg()
        self.config = config
        self.cross_check = self.config.cross_check
        self.num_samples = self.config.num_samples
        self.min_dist_pts = self.config.min_dist_pts
        self.top_k_candidates = self.config.top_k_candidates
        self.grid_size = self.config.grid_size
        self.line_score = self.config.line_score

    def forward(
        self, line_seg1: torch.Tensor, line_seg2: torch.Tensor, desc1: torch.Tensor, desc2: torch.Tensor
    ) -> torch.Tensor:
        """Find the best matches between two sets of line segments and their corresponding descriptors."""
        KORNIA_CHECK_SHAPE(line_seg1, ["N", "2", "2"])
        KORNIA_CHECK_SHAPE(line_seg2, ["N", "2", "2"])
        KORNIA_CHECK_SHAPE(desc1, ["B", "D", "H", "H"])
        KORNIA_CHECK_SHAPE(desc2, ["B", "D", "H", "H"])
        device = desc1.device
        img_size1 = (desc1.shape[2] * self.grid_size, desc1.shape[3] * self.grid_size)
        img_size2 = (desc2.shape[2] * self.grid_size, desc2.shape[3] * self.grid_size)

        # Default case when an image has no lines
        if len(line_seg1) == 0:
            return torch.empty(0, dtype=torch.int, device=device)
        if len(line_seg2) == 0:
            return -torch.ones(len(line_seg1), dtype=torch.int, device=device)

        # Sample points regularly along each line
        line_points1, valid_points1 = self.sample_line_points(line_seg1)
        line_points2, valid_points2 = self.sample_line_points(line_seg2)
        line_points1 = line_points1.reshape(-1, 2)
        line_points2 = line_points2.reshape(-1, 2)

        # Extract the descriptors for each point
        grid1 = keypoints_to_grid(line_points1, img_size1)
        grid2 = keypoints_to_grid(line_points2, img_size2)
        desc1 = F.normalize(F.grid_sample(desc1, grid1, align_corners=False)[0, :, :, 0], dim=0)
        desc2 = F.normalize(F.grid_sample(desc2, grid2, align_corners=False)[0, :, :, 0], dim=0)

        # Precompute the distance between line points for every pair of lines
        # Assign a score of -1 for invalid points
        scores = desc1.t() @ desc2
        scores[~valid_points1.flatten()] = -1
        scores[:, ~valid_points2.flatten()] = -1
        scores = scores.reshape(len(line_seg1), self.num_samples, len(line_seg2), self.num_samples)
        scores = scores.permute(0, 2, 1, 3)
        # scores.shape = (n_lines1, n_lines2, num_samples, num_samples)

        # Pre-filter the line candidates and find the best match for each line
        matches = self.filter_and_match_lines(scores)

        # [Optionally] filter matches with mutual nearest neighbor filtering
        if self.cross_check:
            matches2 = self.filter_and_match_lines(scores.permute(1, 0, 3, 2))
            mutual = matches2[matches] == torch.arange(len(line_seg1), device=device)
            matches[~mutual] = -1

        return matches

    def sample_line_points(self, line_seg: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """Regularly sample points along each line segments, with a minimal distance between each point.

        Pad the remaining points.

        Args:
            line_seg: an Nx2x2 torch.Tensor.

        Returns:
            line_points: an N x num_samples x 2 torch.Tensor.
            valid_points: a boolean N x num_samples torch.Tensor.
        """
        _N, _, _ = line_seg.shape
        M = self.num_samples
        dev = line_seg.device

        lengths = torch.norm(line_seg[:, 0] - line_seg[:, 1], dim=1)
        num_pts = torch.clamp((lengths / self.min_dist_pts).floor().int(), min=2, max=M)  # (N,)

        orig = line_seg[:, 0].unsqueeze(1)
        dirs = (line_seg[:, 1] - line_seg[:, 0]).unsqueeze(1)
        idx = torch.arange(M, device=dev).unsqueeze(0)
        denom = (num_pts - 1).unsqueeze(1)
        alpha = idx / denom
        pts = orig + dirs * alpha.unsqueeze(-1)
        valid = idx < num_pts.unsqueeze(1)
        pts = pts.masked_fill(~valid.unsqueeze(-1), 0.0)

        return pts, valid

    def filter_and_match_lines(self, scores: torch.Tensor) -> torch.Tensor:
        """Use scores to keep the top k best lines.

        Compute the Needleman- Wunsch algorithm on each candidate pairs, and keep the highest score.

        Args:
            scores: a (N, M, n, n) torch.Tensor containing the pairwise scores
                    of the elements to match.

        Returns:
            matches: a (N) torch.Tensor containing the indices of the best match
        """
        KORNIA_CHECK_SHAPE(scores, ["M", "N", "n", "n"])

        # Pre-filter the pairs and keep the top k best candidate lines
        line_scores1 = scores.max(3)[0]
        valid_scores1 = line_scores1 != -1
        line_scores1 = (line_scores1 * valid_scores1).sum(2) / valid_scores1.sum(2)
        line_scores2 = scores.max(2)[0]
        valid_scores2 = line_scores2 != -1
        line_scores2 = (line_scores2 * valid_scores2).sum(2) / valid_scores2.sum(2)
        line_scores = (line_scores1 + line_scores2) / 2
        topk_lines = torch.argsort(line_scores, dim=1)[:, -self.top_k_candidates :]
        # topk_lines.shape = (n_lines1, top_k_candidates)

        top_scores = torch.take_along_dim(scores, topk_lines[:, :, None, None], dim=1)

        # Consider the reversed line segments as well
        top_scores = torch.cat([top_scores, torch.flip(top_scores, dims=[-1])], 1)

        # Compute the line distance matrix with Needleman-Wunsch algo and
        # retrieve the closest line neighbor
        n_lines1, top2k, n, m = top_scores.shape
        top_scores = top_scores.reshape((n_lines1 * top2k, n, m))
        nw_scores = self.needleman_wunsch(top_scores)
        nw_scores = nw_scores.reshape(n_lines1, top2k)
        matches = torch.remainder(torch.argmax(nw_scores, dim=1), top2k // 2)
        matches = topk_lines[torch.arange(n_lines1), matches]
        return matches

    def needleman_wunsch(self, scores: torch.Tensor) -> torch.Tensor:
        """Batched implementation of the Needleman-Wunsch algorithm.

        The cost of the InDel operation is set to 0 by subtracting the gap
        penalty to the scores.

        Args:
            scores: a (B, N, M) torch.Tensor containing the pairwise scores
                    of the elements to match.
        """
        KORNIA_CHECK_SHAPE(scores, ["B", "N", "M"])
        # Recalibrate the scores to get a gap score of 0
        gap = 0.1
        B, N, M = scores.shape
        dp = torch.zeros(B, N + 1, M + 1, device=scores.device)
        S = scores - gap
        for k in range(2, N + M + 1):
            i_min = max(1, k - M)
            i_max = min(N, k - 1)
            i = torch.arange(i_min, i_max + 1, device=scores.device)
            j = k - i
            up = dp[:, i - 1, j]
            left = dp[:, i, j - 1]
            torch.diag = dp[:, i - 1, j - 1] + S[:, i - 1, j - 1]
            dp[:, i, j] = torch.max(torch.max(up, left), torch.diag)
        return dp[:, -1, -1]


def keypoints_to_grid(keypoints: torch.Tensor, img_size: Tuple[int, int]) -> torch.Tensor:
    """Convert a list of keypoints into a grid in [-1, 1]² that can be used in torch.nn.functional.interpolate.

    Args:
        keypoints: a torch.Tensor [N, 2] of N keypoints (ij coordinates convention).
        img_size: the original image size (H, W)

    """
    KORNIA_CHECK_SHAPE(keypoints, ["N", "2"])
    n_points = len(keypoints)
    grid_points = normalize_pixel_coordinates(keypoints[:, [1, 0]], img_size[0], img_size[1])
    grid_points = grid_points.view(-1, n_points, 1, 2)
    return grid_points


def batched_linspace(start: torch.Tensor, end: torch.Tensor, step: int, dim: int) -> torch.Tensor:
    """Batch version of torch.F.normalize (similar to the numpy one)."""
    intervals = ((end - start) / (step - 1)).unsqueeze(dim)
    broadcast_size = [1] * len(intervals.shape)
    broadcast_size[dim] = step
    samples = torch.arange(step, dtype=torch.float, device=start.device).reshape(broadcast_size)
    samples = start.unsqueeze(dim) + samples * intervals
    return samples
